Deep learning methodology | AkhilML

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Complete Machine Learning, Business analytics & Data Science with R programming & Python

Complete machine learning & data science course Introduction

Course introduction (5:21)
How to get help for data science (4:19)
Data science & machine learning as career option (8:12)
How to make right decisions for your career in data science & machine learning (11:39)
Various Job options for aspiring data scientists & machine learning engineers (9:34)
AI Vs ML Vs DL with Types of machine learning (12:37)
Resources

Hands-on R programming for machine learning & data science

R Introduction with installation of rstudio (17:02)
Vectors, Matrix & Data frame (6:56)
Data Types in R (15:30)
Variables & Objects in R (9:57)
Comments & Vectors in R (8:51)
Data wrangling with R- Part 1 (9:26)
Data wrangling with R-Part 2 (14:20)
Operators in R-Part 1 (10:57)
Operators in R-Part 2 (3:13)
Loops in R (17:16)
If Else conditional blocks in R (9:48)
Functions in R (13:41)
Assignment for R (7:23)

Machine learning fundamentals

Reading various files with R (17:19)
Data pre-processing introduction- selection & manipulation (8:46)
Data selection & manipulation-Rows & Columns (6:26)
Data selection & manipulation with Dplyr (6:00)
Data selection & manipulation with Arrange & Mutate (7:47)
Data selection & manipulation with Subset & Merge (4:47)
Data selection & manipulation-Handling missing data (15:09)
Data manipulation & selection assignment (2:27)
Machine learning fundamentals Quiz

Data visualization with R

Data visualization with R- introduction (5:16)
Histogram vs bar plot with plotting missing values (6:16)
Bar plots and Histograms with R (5:59)
Horizontal bar plots and Plot function (5:59)
More on Plot function with heat map (5:01)
Boxplot with Pair & Par commands (10:19)
Line graphs & maps (5:47)
GGPlot2 introduction (9:30)
Data visualization with GGPlot2 (3:14)
Lattice and Scatter3d plot libraries (10:33)
Assignment (1:59)
Data Visualizations Quiz

Applied Statistics for Machine learning

Introduction to applied statistics with Variables and Sample Size (10:53)
Descriptive vs Inferential analysis (5:32)
Mean, Median, Mode and Range (9:14)
Variance and Standard deviation (5:54)
Standard Error- Skewness with Kurtosis (4:24)
P value with confidence interval (7:45)
T test and F ratio (6:36)
Hypothesis testing (6:34)
Quiz

Machine learning fundamentals

Machine learning fundamentals (13:58)
Regression fundamentals (3:03)
Classification fundamentals (2:36)
Fundamentals of dimension reduction and data reduction models (5:01)

Evaluation Metrics for Regression & Classification Algorithms

MAE Vs MSE Vs RMSE vs RMSLE (14:37)

ANOVA with R

ANOVA introduction & fundamentals (12:19)
ANOVA in R (16:27)
ANOVA project (3:07)

Linear Regression with R

Fundamentals of Linear regression (21:33)
Implementation of linear regression in R (21:44)
Linear regression project (1:22)

Logistic Regression with R

Fundamentals of Logistic regression (5:23)
Logistic Regression with R- Part 1- Data Wrangling (14:42)
Logistic regression with R-Part 2 Data Wrangling and visualization (10:35)
Logistic regression with R-Part 3 Conclusion with Prediction (18:24)
Logistic regression project (2:17)

Dimension reduction technique with Principal component analysis

Fundamentals of Dimension reduction technique with principal component analysis (13:53)
PCA implementation in r with princomp (17:03)
PCA project (1:18)

Clustering with K-Means

Fundamentals of clustering with K-Means (15:08)
K-Means implementation in r (26:08)
K-Means Project (0:30)

Tree based models- CART technique & Random Forest

Fundamentals of Decision tree and CART technique (12:29)
CART Implementation in R (22:59)
Fundamentals of Ensemble techniques with Random Forest machine learning model (12:01)
Random Forest with R (17:13)
Random Forest Project (3:28)

KNN with R

Fundamentals of KNN (4:03)
Implementation of KNN in R (8:25)
KNN Project (1:01)

Naive Bayes

Naive bayes fundamentals and implementation in r (11:35)
Naive bayes project (2:40)

Neural Networks with R

Fundamentals of Neural Networks (9:14)
Implementation of Neural networks with R (15:54)
Neural Network Project (2:31)

Machine learning & Data science with Python

Introduction of Machine learning with Python (3:00)
Python Tool box for Machine learning (4:28)
Anaconda distribution's walk through & environment (7:37)
Jupyter notebook's walk through (6:51)
Virtual Environment introduction & setup (4:52)
Environment setup with Anaconda Prompt (13:37)
Environment setup with Anaconda navigator (8:17)
Numpy Introduction (11:42)
Operations with Numpy Objects (9:17)
Data selection with Numpy (15:23)
Pandas Overview (11:49)
Data analysis with Python & Pandas (21:38)

Data Visualization with Python-Pandas, Matplotlib & Seaborn

Data Visualization with Pandas (9:07)
Data visualization with Matplotlib (8:40)
Data Visualization with Seaborn (11:07)

Multi class Linear regression with Python & Scikit learn

Linear Regression with Python- Part 1 (23:05)
Linear Regression with Python- Part 2 (20:58)

Logistic regression with Python

Introduction of Logistic regression with Python (7:50)
How to use label encoding & one hot encoding in Logistic regression (13:15)
How to handle multicollinearity in logistic regression (8:24)
How to optimize performance with regularization & grid search (12:23)

Deep learning with Image recognition+Python+Keras+MLP

Introduction to Deep learning (7:10)
Fundamentals of deep learning (6:33)
Deep learning methodology (10:01)
Deep learning architecture (7:48)
Why Activation function in deep learning (8:08)
Relu & Softmax activation functions with Keras introduction (9:14)
Image recognition with Python, Keras & MLP- Part 1 (6:12)
Image recognition with Python, Keras & MLP- Part 2 (9:08)
Image recognition with Python, Keras & MLP- Part 3 (8:30)
Image recognition with Python, Keras & MLP- Part 4 (14:12)

Deep learning methodology

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